This application relates generally to analysis of geophysical data. More specifically, this application relates to methods and systems that use cross-correlation techniques in the analysis of geophysical data.
There are a number of applications in which it is desirable to identify geological structures. For example, in many instances, the presence of subsurface geological structures such as faults or other stratigraphic features may be used in identifying locations of hydrocarbon reservoirs so that drilling sites may be specified. Typically, the identification of geological structures begins with the collection of geophysical data, which may be in the form of seismic or other types of geophysical data. For example, seismic data may be collected by distributing acoustic sources at an area and using the sources to synthesize physical shock waves that reflect off rock strata at variable velocities and return to the surface. Geophones at the surface measure and record ground motion to define a seismic response in the form of a data set.
The collected data are subsequently analyzed, with the reliability of interpretations made from the data being very much tied to the manner of analysis. There have accordingly been a significant number of attempts to develop reliable analytical techniques for geophysical data that have good predictive power. Many of these approaches are based on pattern-recognition techniques and may incorporate various artificial-intelligence techniques such as neural nets, experts systems, genetic algorithms, and the like. A common feature of these techniques is that they attempt to identify some attribute that may be extracted from the data and to which the pattern-recognition approaches may be applied. These techniques accordingly require a relatively high level of user input in their execution and, in practice, their reliability has tended to be disappointing. A basic deficiency of all such techniques is their dependence on identifying an attribute that may be extracted from the data that provides a parameter set having suitably predictive power; the ability to do so has been constrained by the variation and complexity of data that arise from diverse real-world applications.
There is accordingly a general need in the art for improved methods and systems for analyzing geophysical data.